We consider binary classifiers \( c : \mathcal {X} \rightarrow \mathbb {R}\) . The predicted class is denoted by \( \textrm {sign}(c(x)) \in \lbrace  -1, 1 \rbrace  \) . The class our models predict is related to distinguishing between the mollifier's distribution and the target distribution. It is not `carved' in the training sample's features and thereby does not carry the same risk for unfairness as in [1]}.
We assume boundedness on the output of \( c \) : \( c(x) \in [-C, C] \)  for some \(C>0\) . We also require a variant of the all important weak learning assumption of boosting.
